In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

In the body, mucus provides an important defense mechanism by limiting the penetration of pathogens. It is therefore also a major obstacle for the efficient delivery of particle-based drug carriers. The acidic stomach lining in particular is difficult to overcome because mucin glycoproteins form viscoelastic gels under acidic conditions. The bacterium Helicobacter pylori has developed a strategy to overcome the mucus barrier by producing the enzyme urease, which locally raises the pH and consequently liquefies the mucus. This allows the bacteria to swim through mucus and to reach the epithelial surface. We present an artificial system of reactive magnetic micropropellers that mimic this strategy to move through gastric mucin gels by making use of surface-immobilized urease. The results demonstrate the validity of this biomimetic approach to penetrate biological gels, and show that externally propelled microstructures can actively and reversibly manipulate the physical state of their surroundings, suggesting that such particles could potentially penetrate native mucus.

We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

The Electrochemical Pen (EChemPen) was developed as an attractive tool for learning electrochemistry. The fabrication, principle, and operation of the EChemPen are simple and can be easily performed by students in practical classes. It is based on a regular fountain pen principle, where the electrolytic solution is dispensed at a tip to locally modify a conductive surface by triggering a localized electrochemical reaction. Three simple model reactions were chosen to demonstrate the versatility of the EChemPen for teaching various electrochemical processes. We describe first the reversible writing/erasing of metal letters, then the electrodeposition of a black conducting polymer "ink", and finally the colorful writings that can be generated by titanium anodization and that can be controlled by the applied potential. These entertaining and didactic experiments are adapted for teaching undergraduate students that start to study electrochemistry by means of surface modification reactions.

Optimal fiber designs for the maximal pull-off force have been indispensable for increasing the attachment performance of recently introduced gecko-inspired reversible micro/nanofibrillar adhesives. There are several theoretical studies on such optimal designs; however, due to the lack of three-dimensional (3D) fabrication techniques that can fabricate such optimal designs in 3D, there have not been many experimental investigations on this challenge. In this study, we benefitted from recent advances in two-photon lithography techniques to fabricate mushroomlike polyurethane elastomer fibers with different aspect ratios of tip to stalk diameter (β) and tip wedge angles (θ) to investigate the effect of these two parameters on the pull-off force. We found similar trends to those predicted theoretically. We found that β has an impact on the slope of the force-displacement curve while both β and θ play a role in the stress distribution and crack propagation. We found that these effects are coupled and the optimal set of parameters also depends on the fiber material. This is the first experimental verification of such optimal designs proposed for mushroomlike microfibers. This experimental approach could be used to evaluate a wide range of complex microstructured adhesive designs suggested in the literature and optimize them.

To look human, digital full-body avatars need to have soft tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and a method that accurately registers a template mesh to sequences of 3D scans. Using over 40,000 scans of ten subjects, we learn how soft tissue motion causes mesh triangles to deform relative to a base 3D body model. Our Dyna model uses a low-dimensional linear subspace to approximate soft-tissue deformation and relates the subspace coefficients to the changing pose of the body. Dyna uses a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. We provide tools for animators to modify the deformations and apply them to new stylized characters.

Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information.
Significance Statement: The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

The last decade has seen an increasing number of studies developing bacteria and other cell-integrated biohybrid microsystems. However, the highly stochastic motion of these microsystems severely limits their potential use. Here, we present a method that exploits the pH sensing of flagellated bacteria to realize robust drift control of multi-bacteria propelled microrobots. Under three specifically configured pH gradients, we demonstrate that the microrobots exhibit both unidirectional and bidirectional pH-tactic behaviors, which are also observed in free-swimming bacteria. From trajectory analysis, we find that the swimming direction and speed biases are two major factors that contribute to their tactic drift motion. The motion analysis of microrobots also sheds light on the propulsion dynamics of the flagellated bacteria as bioactuators. It is expected that similar driving mechanisms are shared among pH-taxis, chemotaxis, and thermotaxis. By identifying the mechanism that drives the tactic behavior of bacteria-propelled microsystems, this study opens up an avenue towards improving the control of biohybrid microsystems. Furthermore, assuming that it is possible to tune the preferred pH of bioactuators by genetic engineering, these biohybrid microsystems could potentially be applied to sense the pH gradient induced by cancerous cells in stagnant fluids inside human body and realize targeted drug delivery.

Locomotion in fluids at the nanoscale is dominated by viscous drag. One efficient propulsion scheme is to use a weak rotating magnetic field that drives a chiral object. Froth bacterial flagella to artificial drills, the corkscrew is a universally useful chiral shape for propulsion in viscous environments. Externally powered magnetic micro- and nanomotors have been recently developed that allow for precise fuel-free propulsion in complex media. Here, we combine analytical and numerical theory with experiments on nanostructured screw-propellers to show that the optimal length is surprisingly short only about one helical turn, which is shorter than most of the structures in use to date. The results have important implications for the design of artificial actuated nano- and micropropellers and can dramatically reduce fabrication times, while ensuring optimal performance.

Detecting and identifying the different objects in an image fast and reliably is an
important skill for interacting with one’s environment. The main problem is that in
theory, all parts of an image have to be searched for objects on many different scales
to make sure that no object instance is missed. It however takes considerable time
and effort to actually classify the content of a given image region and both time
and computational capacities that an agent can spend on classification are limited.
Humans use a process called visual attention to quickly decide which locations of
an image need to be processed in detail and which can be ignored. This allows us
to deal with the huge amount of visual information and to employ the capacities
of our visual system efficiently.
For computer vision, researchers have to deal with exactly the same problems,
so learning from the behaviour of humans provides a promising way to improve
existing algorithms. In the presented master’s thesis, a model is trained with eye
tracking data recorded from 15 participants that were asked to search images for
objects from three different categories. It uses a deep convolutional neural network
to extract features from the input image that are then combined to form a saliency
map. This map provides information about which image regions are interesting
when searching for the given target object and can thus be used to reduce the
parts of the image that have to be processed in detail. The method is based on a
recent publication of Kümmerer et al., but in contrast to the original method that
computes general, task independent saliency, the presented model is supposed to
respond differently when searching for different target categories.

Flexure-based parallel mechanisms (FPMs) are a type of compliant mechanisms that consist of a rigid end-effector that is articulated by several parallel, flexible limbs (a.k.a. sub-chains). Existing design methods can enhance the FPMs’ dynamic and stiffness properties by conducting a size optimization on their sub-chains. A similar optimization process, however, was not performed for their sub-chains’ topology, and this may severely limit the benefits of a size optimization. Thus, this paper proposes to use a structural optimization approach to synthesize and optimize the topology, shape and size of the FPMs’ sub-chains. The benefits of this approach are demonstrated via the design and development of a planar X − Y − θz FPM. A prototype of this FPM was evaluated experimentally to have a large workspace of 1.2 mm × 1.2 mm × 6°, a fundamental natural frequency of 102 Hz, and stiffness ratios that are greater than 120. The achieved properties show significant improvement over existing 3-degrees-of-freedom compliant mechanisms that can deflect more than 0.5 mm and 0.5°. These compliant mechanisms typically have stiffness ratios that are less than 60 and a fundamental natural frequency that is less than 45 Hz.

For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial
for successful controlling its motion. Often, pose estimations can be acquired from encoders
inside the arm, but they can have significant inaccuracy which makes the use of additional
techniques necessary.
In this master thesis, a novel approach of robot arm pose estimation is presented, that works on
single depth images without the need of prior foreground segmentation or other preprocessing
steps.
A random regression forest is used, which is trained only on synthetically generated data.
The approach improves former work by Bohg et al. by considerably reducing the computational
effort both at training and test time. The forest in the new method directly estimates the
desired joint angles while in the former approach, the forest casts 3D position votes for the
joints, which then have to be clustered and fed into an iterative inverse kinematic process to
finally get the joint angles.
To improve the estimation accuracy, the standard training objective of the forest training is
replaced by a specialized function that makes use of a model-dependent distance metric, called
DISP.
Experimental results show that the specialized objective indeed improves pose estimation and
it is shown that the method, despite of being trained on synthetic data only, is able to
provide reasonable estimations for real data at test time.

Two recently predicted nuclear magnetic resonance effects, the chirality-induced rotating electric polarization and the oscillating magnetization, are examined for several experimentally available chiral molecules. We discuss in detail the requirements for experimental detection of chirality-sensitive NMR effects of the studied molecules. These requirements are related to two parameters: the shielding polarizability and the antisymmetric part of the nuclear magnetic shielding tensor. The dominant second contribution has been computed for small molecules at the coupled cluster and density functional theory levels. It was found that DFT calculations using the KT2 functional and the aug-cc-pCVTZ basis set adequately reproduce the CCSD(T) values obtained with the same basis set. The largest values of parameters, thus most promising from the experimental point of view, were obtained for the fluorine nuclei in 1,3-difluorocyclopropene and 1,3-diphenyl-2-fluoro-3-trifluoromethylcyclopropene.

Host-guest inclusion complexes are abundant in molecular systems and of fundamental importance in living organisms. Realizing a colloidal analogue of a molecular dynamic inclusion complex is challenging because inorganic nanoparticles (NPs) with a well-defined cavity and portal are difficult to synthesize in high yield and with good structural fidelity. Herein, a generic strategy towards the fabrication of dynamic 1: 1 inclusion complexes of metal nanoparticles inside oxide nanocups with high yield (> 70%) and regiospecificity (> 90%) by means of a reactive double Janus nanoparticle intermediate is reported. Experimental evidence confirms that the inclusion complexes are formed by a kinetically controlled mechanism involving a delicate interplay between bipolar galvanic corrosion and alloying-dealloying oxidation. Release of the NP guest from the nanocups can be efficiently triggered by an external stimulus. Featured cover article.

In this thesis we address the problem of building shape models of the human body,
in 2D and 3D, which are realistic and efficient to use. We focus our efforts on the
human body, which is highly articulated and has interesting shape variations, but
the approaches we present here can be applied to generic deformable and articulated
objects. To address efficiency, we constrain our models to be part-based and have a
tree-structured representation with pairwise relationships between connected parts.
This allows the application of methods for distributed inference based on message
passing. To address realism, we exploit recent advances in computer graphics that
represent the human body with statistical shape models learned from 3D scans.
We introduce two articulated body models, a 2D model, named Deformable Structures
(DS), which is a contour-based model parameterized for 2D pose and projected
shape, and a 3D model, named Stitchable Puppet (SP), which is a mesh-based model
parameterized for 3D pose, pose-dependent deformations and intrinsic body shape.
We have successfully applied the models to interesting and challenging problems in
computer vision and computer graphics, namely pose estimation from static images,
pose estimation from video sequences, pose and shape estimation from 3D scan data.
This advances the state of the art in human pose and shape estimation and suggests
that carefully dened realistic models can be important for computer vision.
More work at the intersection of vision and graphics is thus encouraged.

Cells in tissues encounter a range of physical cues as they migrate. Probing single cell and collective migratory responses to physically defined three-dimensional (3D) microenvironments and the factors that modulate those responses are critical to understanding how tissue migration is regulated during development, regeneration, and cancer. One key physical factor that regulates cell migration is topography. Most studies on surface topography and cell mechanics have been carried out with single migratory cells, yet little is known about the spreading and motility response of 3D complex multi-cellular tissues to topographical cues. Here, we examine the response to complex topographical cues of microsurgically isolated tissue explants composed of epithelial and mesenchymal cell layers from naturally 3D organized embryos of the aquatic frog Xenopus laevis. We control topography using fabricated micropost arrays (MPAs) and investigate the collective 3D migration of these multi-cellular systems in these MPAs. We find that the topography regulates both collective and individual cell migration and that dense MPAs reduce but do not eliminate tissue spreading. By modulating cell size through the cell cycle inhibitor Mitomycin C or the spacing of the MPAs we uncover how 3D topographical cues disrupt collective cell migration. We find surface topography can direct both single cell motility and tissue spreading, altering tissue-scale processes that enable efficient conversion of single cell motility into collective movement.

Fabrication schemes that integrate inorganic microstructures with hydrogel substrates are essential for advancing flexible electronics. A transfer printing process that is made possible through the design and synthesis of adhesion-promoting hydrogels as target substrates is reported. This fabrication technique may advance ultracompliant electronics by melding microfabricated structures with swollen hydrogel substrates.

We demonstrate a simple physical fabrication method to control surface roughness of Janus micromotors and fabricate self-propelled active Janus microparticles with rough catalytic platinum surfaces that show a four-fold increase in their propulsion speed compared to conventional Janus particles coated with a smooth Pt layer.

Untethered robots miniaturized to the length scale of millimeter and below attract growing attention for the prospect of transforming many aspects of health care and bioengineering. As the robot size goes down to the order of a single cell, previously inaccessible body sites would become available for high-resolution in situ and in vivo manipulations. This unprecedented direct access would enable an extensive range of minimally invasive medical operations. Here, we provide a comprehensive review of the current advances in biomedical untethered mobile milli/microrobots. We put a special emphasis on the potential impacts of biomedical microrobots in the near future. Finally, we discuss the existing challenges and emerging concepts associated with designing such a miniaturized robot for operation inside a biological environment for biomedical applications.

As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).

New scanning technologies are increasing the importance of 3D mesh data, and of algorithms that can reliably register meshes obtained from multiple scans. Surface registration is important e.g. for building full 3D models from partial scans, identifying and tracking objects in a 3D scene, creating statistical shape models.
Human body registration is particularly important for many applications, ranging from biomedicine and robotics to the production of movies and video games; but obtaining accurate and reliable registrations is challenging, given the articulated, non-rigidly deformable structure of the human body.
In this thesis, we tackle the problem of 3D human body registration.
We start by analyzing the current state of the art, and find that: a) most registration techniques rely only on geometric information, which is ambiguous on flat surface areas; b) there is a lack of adequate datasets and benchmarks in the field. We address both issues.
Our contribution is threefold. First, we present a model-based registration technique for human meshes that combines geometry and surface texture information to provide highly accurate mesh-to-mesh correspondences. Our approach estimates scene lighting and surface albedo, and uses the albedo to construct a high-resolution textured 3D body model that is brought into registration with multi-camera image data using a robust matching term.
Second, by leveraging our technique, we present FAUST (Fine Alignment Using Scan Texture), a novel dataset collecting 300 high-resolution scans of 10 people in a wide range of poses. FAUST is the first dataset providing both real scans and automatically computed, reliable "ground-truth" correspondences between them.
Third, we explore possible uses of our approach in dermatology. By combining our registration technique with a melanocytic lesion segmentation algorithm, we propose a system that automatically detects new or evolving lesions over almost the entire body surface, thus helping dermatologists identify potential melanomas.
We conclude this thesis investigating the benefits of using texture information to establish frame-to-frame correspondences in dynamic monocular sequences captured with consumer depth cameras. We outline a novel approach to reconstruct realistic body shape and appearance models from dynamic human performances, and show preliminary results on challenging sequences captured with a Kinect.

The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.

Long Range Motion Estimation and Applications, University of Massachusetts Amherst, University of Massachusetts Amherst, Febuary 2015 (phdthesis)

Abstract

Finding correspondences between images underlies many computer vision problems, such as optical flow, tracking, stereovision and alignment. Finding these correspondences involves formulating a matching function and optimizing it. This optimization process is often gradient descent, which avoids exhaustive search, but relies on the assumption of being in the basin of attraction of the right local minimum. This is often the case when the displacement is small, and current methods obtain very accurate results for small motions. However, when the motion is large and the matching function is bumpy this assumption is less likely to be true. One traditional way of avoiding this abruptness is to smooth the matching function spatially by blurring the images. As the displacement becomes larger, the amount of blur required to smooth the matching function becomes also larger. This averaging of pixels leads to a loss of detail in the image. Therefore, there is a trade-off between the size of the objects that can be tracked and the displacement that can be captured.
In this thesis we address the basic problem of increasing the size of the basin of attraction in a matching function. We use an image descriptor called distribution fields (DFs). By blurring the images in DF space instead of in pixel space, we in- crease the size of the basin attraction with respect to traditional methods. We show competitive results using DFs both in object tracking and optical flow. Finally we demonstrate an application of capturing large motions for temporal video stitching.

We demonstrate a chemically driven, autonomous catalytic microdrill. An asymmetric distribution of catalyst causes the helical swimmer to twist while it undergoes directed propulsion. A driving torque and hydrodynamic coupling between translation and rotation at low Reynolds number leads to drill-like swimming behaviour.

Cells in tissues encounter a range of physical cues as they migrate. Probing single cell and collective migratory responses to physically defined three-dimensional (3D) microenvironments and the factors that modulate those responses are critical to understanding how tissue migration is regulated during development, regeneration, and cancer. One key physical factor that regulates cell migration is topology. Most studies on surface topology and cell mechanics have been carried out with single migratory cells, yet little is known about the spreading and motility response of 3D complex multicellular tissues to topological cues. Here, we examine the behaviors of microsurgically isolated tissue explants composed of epithelial and mesenchymal cell layers from naturally 3D organized embryos of the aquatic frog Xenopus laevis to complex topological cues. We control topology using fabricated micropost arrays (MPAs) with different diameters (e.g., different spacing gaps) and investigate the collective 3D migration of these multicellular systems in these MPAs. Our topographical controlled approach for cellular application enables us to achieve a high degree of control over micropost positioning and geometry via simple, accurate, and repeatable microfabrication processes. We find that the topology regulates both collective and individual cell migration and that dense MPAs reduce but do not eliminate tissue spreading. By modulating cell size through the cell cycle inhibitor Mitomycin C or the spacing within MPAs we discover a role for topology in disrupting collective enhancement of cell migration. We find 3D topological cues can direct both single cell motility and tissue spreading, altering tissue-scale processes that enable efficient conversion of single cell motility into collective movement.

Lab on a Chip, 15(7):1667-1676, Royal Society of Chemistry, January 2015 (article)

Abstract

Three-dimensional (3D) heterogeneous assembly of coded microgels in enclosed aquatic environments is demonstrated using a remotely actuated and controlled magnetic microgripper by a customized electromagnetic coil system. The microgripper uses different ‘stick–slip’ and ‘rolling’ locomotion in 2D and also levitation in 3D by magnetic gradient-based pulling force. This enables the microrobot to precisely manipulate each microgel by controlling its position and orientation in all x–y–z directions. Our microrobotic assembly method broke the barrier of limitation on the number of assembled microgel layers, because it enabled precise 3D levitation of the microgripper. We used the gripper to assemble microgels that had been coded with different colours and shapes onto prefabricated polymeric microposts. This eliminates the need for extra secondary cross-linking to fix the final construct. We demonstrated assembly of microgels on a single micropost up to ten layers. By increasing the number and changing the distribution of the posts, complex heterogeneous microsystems were possible to construct in 3D.

This paper introduces a new design approach to synthesize multiple degrees-of-freedom (DOF) flexure-based parallel mechanism (FPM). Termed as an integrated design approach, it is a systematic design methodology, which integrates both classical mechanism synthesis and modern topology optimization technique, to deliver an optimized multi-DOF FPM. This design approach is separated into two levels. At sub-chain level, a novel topology optimization technique, which uses the classical linkage mechanisms as DNA seeds, is used to synthesize the compliant joints or limbs. At configuration level, the optimal compliant joints are used to form the parallel limbs of the multi-DOF FPM and another stage of optimization was conducted to determine the optimal space distribution between these compliant joints so as to generate a multi-DOF FPM with optimized stiffness characteristic. In this paper, the design of a 3-DOF planar motion FPM was used to demonstrate the effectiveness and accuracy of this proposed design approach.

In this letter, we propose a technique by which we can actively adjust frictional properties of elastic fibrillar structures in different directions. Using a mesh attached to a two degree-of-freedom linear stage, we controlled the active length and the tilt angle of fibers, independently. Thus, we were able to achieve desired levels of friction forces in different directions and significantly improve passive friction anisotropies observed in the same fiber arrays. The proposed technique would allow us to readily control the friction anisotropy and the friction magnitude of fibrillar structures in any planar direction.

We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems